Data integration using automated data processing based on target metadata

ABSTRACT

Approaches for data integration between multiple IT sources using automated data processing based on target metadata are provided. Specifically, an integration component is configured to load a mapped data set into a table with delta processing based on a configuration object containing, e.g., the source data location, target table name, and source to target mapping. The integration component uses the target metadata to validate data, identify changes, generate the necessary database programming language (e.g., structured query language (SQL)), and run the database programming language with data binding to perform the actual data updates. The integration component leverages the data target metadata to automate the processing of source data, thus providing a way to validate the data, and identify delta changes at the field level between the source and target. This significantly reduces the overall development effort, while providing consistency in record handling and error reporting.

The present patent document is a continuation of U.S. patent applicationSer. No. 13/832,645, filed Mar. 15, 2013, entitled “DATA INTEGRATIONUSING AUTOMATED DATA PROCESSING BASED ON TARGET METADATA”, thedisclosure of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to data processing, and moreparticularly to automated data processing based on target metadata in adata integration process.

BACKGROUND OF THE INVENTION

In many large IT environments, requirements exist for transporting datain and out of individual systems (e.g., data bridges) as a form ofintegration. Tools used to transport data generally fall into thecategory of ETL (extract, transform, load) tools. ETL is a process indata warehousing that involves extracting data from outside sources,transforming the data in accordance with particular business needs, andloading the data into a data warehouse. An ETL process typically beginswith a user defining a data flow that defines data transformationactivities that extract data from, e.g., flat files or relationaltables, transform the data, and load the data into a data warehouse,data mart, or staging table(s). A data flow, therefore, typicallyincludes a sequence of operations modeled as data flowing from varioustypes of sources, through various transformations, and finally ending inone or more targets.

Prior art ETL approaches require the creation of multiple redundantprocesses, e.g., one for each table or data set. This is especially truewhen using GUI tools of ETL products. The GUI tools make it very easy tomove data between systems. However, when there are complex requirements,such as the need to identify what has changed between the source and thetarget, the ETL tools, and even custom scripts, require a lot ofmodification. This results in the exponential growth of the code base(or process nodes).

Therefore, when developing a data bridge between two informationsystems, one of the biggest challenges is the handling of individualelements. Most ETL tools or batch frameworks provide powerful functions,yet a developer still has to code individually on each data object toperform common tasks such as data validation, record comparison, etc.The process is error prone due to typos, changes in requirements (e.g.,go back and adjust every object), etc. Accordingly, what is needed is asolution that solves at least one of the above-identified deficiencies.

SUMMARY OF THE INVENTION

In general, embodiments of the invention provide data integrationbetween multiple IT sources using automated data processing based ontarget metadata. Specifically, an integration component is configured toload a mapped data set into a table with delta processing based on aconfiguration object containing, e.g., the source data location, targettable name, and source to target mapping. The integration component usesthe target metadata to validate data, identify changes, generate thenecessary database programming language (e.g., structured query language(SQL)), and run the database programming language with data binding toperform the actual data updates. The integration component leverages thedata target metadata to automate the processing of source data, thusproviding a way to validate the data, and identify delta changes at thefield level between the source and target. This significantly reducesthe overall development effort, while providing consistency in recordhandling and error reporting.

In one embodiment, there is a method for data integration usingautomated data processing based on target metadata. In this embodiment,the method comprises the computer implemented steps of: defining a setof data objects built from target metadata; sorting the set of dataobjects built from target metadata; sorting and validating source data;comparing the set of data objects to the sorted and validated sourcedata to identify differences; generating a set of programming languagestatements to map the source data based on the target metadata; andloading the mapped source data to a target storage destination.

In a second embodiment, there is a system for data integration usingautomated data processing based on target metadata. In this embodiment,the system comprises at least one processing unit, and memory operablyassociated with the at least one processing unit. An integrationcomponent is storable in memory and executable by the at least oneprocessing unit. The integration component comprises an object definerconfigured to define a set of data objects built from target metadata; asorter configured to: sort the set of data objects built from targetmetadata; and sort and validate source data; a comparer configured tocompare the set of data objects to the sorted and validated source datato identify differences; a generator configured to generate a set ofdatabase programming language statements to map the source data based onthe target metadata; and a loader configured to load the mapped sourcedata to a target storage destination.

In a third embodiment, there is a computer-readable storage mediumstoring computer instructions, which when executed, enables a computersystem to provide data integration using automated data processing basedon target metadata. In this embodiment, the computer instructionscomprise: defining a set of data objects built from target metadata;sorting the set of data objects built from target metadata; sorting andvalidating source data; comparing the set of data objects to the sortedand validated source data to identify differences; generating a set ofprogramming language statements to map the source data based on thetarget metadata; and loading the mapped source data to a target storagedestination.

In a fourth embodiment, there is a method for deploying a integrationcomponent for use in a computer system that provides data integrationusing automated data processing based on target metadata. In thisembodiment, a computer infrastructure is provided and is operable to:define a set of data objects built from target metadata; sort the set ofdata objects built from target metadata; sorting and validating sourcedata; compare the set of data objects to the sorted and validated sourcedata to identify differences; generate a set of programming languagestatements to map the source data based on the target metadata; and loadthe mapped source data to a target storage destination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic of an exemplary computing environment in whichelements of the present invention may operate;

FIG. 2 shows a more detailed view of an integration component thatoperates in a database IT environment according to embodiments of theinvention; and

FIG. 3 shows a flow diagram for data integration using automated dataprocessing based on target metadata according to embodiments of theinvention.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention, and therefore should not be considered aslimiting the scope of the invention. In the drawings, like numberingrepresents like elements.

DETAILED DESCRIPTION OF THE INVENTION

The invention will now be described more fully herein with reference tothe accompanying drawings, in which exemplary embodiments are shown.Embodiments described herein provide data integration between multipleIT sources using automated data processing based on target metadata.Specifically, an integration component is configured to load a mappeddata set into a table with delta processing based on a configurationobject containing, e.g., the source data location, target table name,and source to target mapping. The integration component uses the targetmetadata to validate data, identify changes, generate the necessarydatabase programming language (e.g., structured query language (SQL)),and run the database programming language with data binding to performthe actual data updates. The integration component leverages the datatarget metadata to automate the processing of source data, thusproviding a way to validate the data, and identify delta changes at thefield level between the source and target. This significantly reducesthe overall development effort, while providing consistency in recordhandling and error reporting.

This disclosure may be embodied in many different forms and should notbe construed as limited to the exemplary embodiments set forth herein.Rather, these exemplary embodiments are provided so that this disclosurewill be thorough and complete and will fully convey the scope of thisdisclosure to those skilled in the art. In the description, details ofwell-known features and techniques may be omitted to avoid unnecessarilyobscuring the presented embodiments. Reference throughout thisspecification to “one embodiment,” “an embodiment,” or similar languagemeans that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the present invention. Thus, appearances of the phrases “in oneembodiment,” “in an embodiment,” and similar language throughout thisspecification may, but do not necessarily, all refer to the sameembodiment.

Furthermore, the terminology used herein is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of this disclosure. As used herein, the singular forms “a”,“an”, and “the” are intended to include the plural forms as well, unlessthe context clearly indicates otherwise. Furthermore, the use of theterms “a”, “an”, etc., do not denote a limitation of quantity, butrather denote the presence of at least one of the referenced items. Itwill be further understood that the terms “comprises” and/or“comprising”, or “includes” and/or “including”, when used in thisspecification, specify the presence of stated features, regions,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,regions, integers, steps, operations, elements, components, and/orgroups thereof.

Unless specifically stated otherwise, it may be appreciated that termssuch as “processing,” “computing,” “determining,” “evaluating,” or thelike, refer to the action and/or processes of a computer or computingsystem, or similar electronic data center device, that manipulatesand/or transforms data represented as physical quantities (e.g.,electronic) within the computing system's registers and/or memories intoother data similarly represented as physical quantities within thecomputing system's memories, registers or other such informationstorage, transmission or viewing devices. The embodiments are notlimited in this context.

Referring now to FIG. 1, a computerized implementation 100 of thepresent invention will be described in greater detail. As depicted,implementation 100 includes computer system 104 deployed within acomputer infrastructure 102. This is intended to demonstrate, amongother things, that the present invention could be implemented within anetwork environment (e.g., the Internet, a wide area network (WAN), alocal area network (LAN), a virtual private network (VPN), etc.), acloud-computing environment, or on a stand-alone computer system.Communication throughout the network can occur via any combination ofvarious types of communication links. For example, the communicationlinks can comprise addressable connections that may utilize anycombination of wired and/or wireless transmission methods. Wherecommunications occur via the Internet, connectivity could be provided byconventional TCP/IP sockets-based protocol, and an Internet serviceprovider could be used to establish connectivity to the Internet. Stillyet, computer infrastructure 102 is intended to demonstrate that some orall of the components of implementation 100 could be deployed, managed,serviced, etc., by a service provider who offers to implement, deploy,and/or perform the functions of the present invention for others.

Computer system 104 is intended to represent any type of computer systemthat may be implemented in deploying/realizing the teachings recitedherein. In this particular example, computer system 104 represents anillustrative system for data integration using automated data processingbased on target metadata. It should be understood that any othercomputers implemented under the present invention may have differentcomponents/software, but will perform similar functions. As shown,computer system 104 includes a processing unit 106 capable ofcommunicating with a data center 106. Also, shown is memory 108 forstoring an integration component 150, a bus 110, and device interfaces112.

Processing unit 106 refers, generally, to any apparatus that performslogic operations, computational tasks, control functions, etc. Aprocessor may include one or more subsystems, components, and/or otherprocessors. A processor will typically include various logic componentsthat operate using a clock signal to latch data, advance logic states,synchronize computations and logic operations, and/or provide othertiming functions. During operation, processing unit 106 collects androutes signals representing outputs from external devices 115 (e.g., agraphical user interface operated by an end-user) to integrationcomponent 150. The signals can be transmitted over a LAN and/or a WAN(e.g., T1, T3, 56 kb, X.25), broadband connections (ISDN, Frame Relay,ATM), wireless links (802.11, Bluetooth, etc.), and so on. In someembodiments, the signals may be encrypted using, for example, trustedkey-pair encryption. Different sensor systems may transmit informationusing different communication pathways, such as Ethernet or wirelessnetworks, direct serial or parallel connections, USB, Firewire®,Bluetooth®, or other proprietary interfaces. (Firewire is a registeredtrademark of Apple Computer, Inc. Bluetooth is a registered trademark ofBluetooth Special Interest Group (SIG)).

In general, processing unit 106 executes computer program code, such asprogram code for operating integration component 150, which is stored inmemory 108 and/or storage system 116. While executing computer programcode, processing unit 106 can read and/or write data to/from memory 108,storage system 116, and data center 106. Storage system 116 can includeVCRs, DVRs, RAID arrays, USB hard drives, optical disk recorders, flashstorage devices, and/or any other data processing and storage elementsfor storing and/or processing data. Although not shown, computer system104 could also include I/O interfaces that communicate with one or moreexternal devices 115 that enable a user to interact with computer system104 (e.g., a keyboard, a pointing device, a display, etc.).

Referring now to FIG. 2, integration component 150 will be described ingreater detail. In an exemplary embodiment, integration component 150operates with an IT environment 152 to provide data integration usingautomated data processing based on target metadata. Embodiments hereinsolve one or more problems of the prior art by creating a plurality ofgeneric functional components on top of the metadata in the data target.Integration component 150 handles everything that the metadata candetermine, including, but not limited to, data validation, and deltachange identification. It will be appreciated that integration component150 can be used as a stand-alone component, or integrated into anexisting ETL tools.

To accomplish this, integration component 150 comprises an objectdefiner 155 configured to define a set of data objects built from targetmetadata of a target storage destination 154. In one embodiment, objectdefiner 155 is configured to generate a set of objects, e.g., a tabledefinition (i.e., “TableObject”) built from target metadata, a columndefinition (i.e., “TableColumnObject”) built from target metadata, andan object representation (i.e., “TableDataObject”) of a record based onthe table definition and the column definition.

As further shown, integration component 150 comprises a sorter 160configured to retrieve and sort (162) the set of data objects built fromtarget metadata by object definer 155, and to generate a target datasnapshot (164), which represents the whole record set from whichTableDataObject is a part of. Sorter 160 is further configured toretrieve, sort, and validate (166) source data 168. In one embodiment,validation ensures that the data is strongly typed, has correct syntax,is within length boundaries, contains only permitted characters, or thatnumbers are correctly signed and within range boundaries, etc. Sortingis performed on both source data (168) and target data snapshot (164)according to the primary identifier (e.g. Primary Key) of each record inthe respective data set using any sorting mechanisms (e.g. Quicksort,Merge Sort), and the primary identifiers have consistent ordering(ascending or descending) throughout the system.

Next, a comparer 165 is configured to compare (170) the set of dataobjects to the sorted and validated source data 168 to identifydifferences between the two. In an exemplary embodiment, a deltaprocessing approach is used, which identifies records/objects that getadded, deleted, or modified between consecutive data pulls. In thiscase, comparer 165 performs delta processing to identify the differencesbetween the set of data objects and the sorted and validated source data168. Based on this, comparer 165 categorizes the data objects into atleast one of the following categories/buckets: E: failed validation, tobe written to error log (log 4j); I: new records, to be inserted; U:updated records; D: to be deleted; and R: re-insert (previously)logically deleted records.

Next, a generator 175 (e.g., an SQL generator) generates a set ofdatabase programming language (e.g., SQL) statements 178 to map sourcedata 168 based on the target metadata. A loader 180 is configured toload the mapped source data to target storage destination 154. In anexemplary embodiment, loader 175 is configured to batch load (182),e.g., using a JDBC (JAVA Database Connectivity API), the mapped sourcedata to target storage destination 154. In this step, the actual SQLstatements are run with data binding to do the actual updates to targetstorage destination 154.

So according to exemplary embodiments herein, when one wants tophysically load a mapped data set into a table with delta processing,integration component 150 will handle all the processing. All that isrequired is to inform integration component 150 the following via aconfiguration object:

-   -   The source data location    -   Target table name    -   Source to target mapping (not necessary if an ETL tool is used)        Integration component 150 will use the metadata to validate        data, identify changes, generate the necessary SQL, and run the        actual SQL with data binding to do the actual updates.

Compared with traditional approaches, this new delta processing willprovide at least the following benefits: reduced code base (e.g., saveup to 90% coding and development time compared with a typicalimplementation); consistent—all the processes work exactly the same way(e.g. all date fields are validated for proper date format (or blank),and field length is checked against each “char” field); and batchupdates—improved performance (e.g., combined with delta, can improveperformance by 20+ times in some cases).

As described herein, the present invention allows data integration usingautomated data processing based on target metadata. It can beappreciated that the approaches disclosed herein can be used within acomputer system for data integration using automated data processingbased on target metadata, as shown in FIG. 1. In this case, integrationcomponent 150 can be provided, and one or more systems for performingthe processes described in the invention can be obtained and deployed tocomputer infrastructure 102. To this extent, the deployment can compriseone or more of: (1) installing program code on a data center device,such as a computer system, from a computer-readable storage medium; (2)adding one or more data center devices to the infrastructure; and (3)incorporating and/or modifying one or more existing systems of theinfrastructure to enable the infrastructure to perform the processactions of the invention.

The exemplary computer system 104 may be described in the generalcontext of computer-executable instructions, such as program modules,being executed by a computer. Generally, program modules includeroutines, programs, people, components, logic, data structures, and soon that perform particular tasks or implements particular abstract datatypes. Exemplary computer system 104 may be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote computer storage media including memory storagedevices.

The program modules carry out the methodologies disclosed herein, asshown in FIG. 3. Shown is a process 200 for data integration usingautomated data processing based on target metadata, wherein, at 201, aset of data objects built from target metadata are defined. At 202, theset of data objects built from target metadata are sorted. At 203,source data is sorted and validated. At 204, the set of data objects arecompared to the source data to identify differences. At 205, a set ofprogramming language statements are generated to map the source databased on the target metadata. At 206, the mapped source data is loadedinto the target storage destination and process 200 ends.

The flowchart of FIG. 3 illustrates the architecture, functionality, andoperation of possible implementations of systems, methods and computerprogram products according to various embodiments of the presentinvention. In this regard, each block in the flowchart may represent amodule, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the blocks might occur out ofthe order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently. It willalso be noted that each block of flowchart illustration can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

Many of the functional units described in this specification have beenlabeled as modules in order to more particularly emphasize theirimplementation independence. For example, a module may be implemented asa hardware circuit comprising custom VLSI circuits or gate arrays,off-the-shelf semiconductors such as logic chips, transistors, or otherdiscrete components. A module may also be implemented in programmablehardware devices such as field programmable gate arrays, programmablearray logic, programmable logic devices or the like. Modules may also beimplemented in software for execution by various types of processors. Anidentified module or component of executable code may, for instance,comprise one or more physical or logical blocks of computer instructionswhich may, for instance, be organized as an object, procedure, orfunction. Nevertheless, the executables of an identified module need notbe physically located together, but may comprise disparate instructionsstored in different locations which, when joined logically together,comprise the module and achieve the stated purpose for the module.

Further, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, overdisparate memory devices, and may exist, at least partially, merely aselectronic signals on a system or network.

Furthermore, as will be described herein, modules may also beimplemented as a combination of software and one or more hardwaredevices. For instance, a module may be embodied in the combination of asoftware executable code stored on a memory device. In a furtherexample, a module may be the combination of a processor that operates ona set of operational data. Still further, a module may be implemented inthe combination of an electronic signal communicated via transmissioncircuitry.

As noted above, some of the embodiments may be embodied in hardware. Thehardware may be referenced as a hardware element. In general, a hardwareelement may refer to any hardware structures arranged to perform certainoperations. In one embodiment, for example, the hardware elements mayinclude any analog or digital electrical or electronic elementsfabricated on a substrate. The fabrication may be performed usingsilicon-based integrated circuit (IC) techniques, such as complementarymetal oxide semiconductor (CMOS), bipolar, and bipolar CMOS (BiCMOS)techniques, for example. Examples of hardware elements may includeprocessors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. The embodiments are not limited inthis context.

Also noted above, some embodiments may be embodied in software. Thesoftware may be referenced as a software element. In general, a softwareelement may refer to any software structures arranged to perform certainoperations. In one embodiment, for example, the software elements mayinclude program instructions and/or data adapted for execution by ahardware element, such as a processor. Program instructions may includean organized list of commands comprising words, values or symbolsarranged in a predetermined syntax, that when executed, may cause aprocessor to perform a corresponding set of operations.

For example, an implementation of exemplary computer system 104 (FIG. 1)may be stored on or transmitted across some form of computer readablemedia. Computer readable media can be any available media that can beaccessed by a computer. By way of example, and not limitation, computerreadable media may comprise “computer storage media” and “communicationsmedia.”

“Computer-readable storage device” includes volatile and non-volatile,removable and non-removable computer storable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Computer storage device includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by a computer.

“Communication media” typically embodies computer readable instructions,data structures, program modules, or other data in a modulated datasignal, such as carrier wave or other transport mechanism. Communicationmedia also includes any information delivery media.

The term “modulated data signal” means a signal that has one or more ofits characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared, and other wireless media. Combinations of any of the above arealso included within the scope of computer readable media.

It is apparent that there has been provided an approach for dataintegration using automated data processing based on target metadata.While the invention has been particularly shown and described inconjunction with a preferred embodiment thereof, it will be appreciatedthat variations and modifications will occur to those skilled in theart. Therefore, it is to be understood that the appended claims areintended to cover all such modifications and changes that fall withinthe true spirit of the invention.

What is claimed is:
 1. A method for data integration using automateddata processing based on target metadata, the method comprising thecomputer-implemented steps of: defining an intermediate data table thatis separate from a target storage destination built from target metadataof the target storage destination containing a set of data objects;loading a mapped data set containing a set of source data objects from asource into the intermediate data table based on a configuration object;sorting the set of data objects built from target metadata andgenerating a target data snapshot that represents an entire record setfrom which the set of data objects is a part; sorting and validatingsource data in the intermediate data table using the target metadata;comparing the set of data objects to the sorted and validated sourcedata using delta processing to identify differences using the targetmetadata; generating a set of programming language statements to map thedifferences in the source data based on the target metadata using thetarget metadata; and loading the mapped source data to a target storagedestination using the set of programming language statements.
 2. Themethod according to claim 1, the loading the mapped source datacomprising batch loading the mapped source data to the target storagedestination.
 3. The method according to claim 1, further comprising thecomputer-implemented step of categorizing the set of data objects basedon the comparison of the set of data objects to the sorted and validatedsource data.
 4. The method according to claim 3, further comprisingcategorizing the data objects into at least one of the followingcategories: failed validation, new records, updated records, deletedrecords, and re-inserted records.
 5. The method according to claim 1,further comprising performing the delta processing to identify thedifferences between the set of data objects and the sorted and validatedsource data.
 6. A computer system for data integration using automateddata processing based on target metadata, the system comprising: atleast one processing unit; memory operably associated with the at leastone processing unit; and an integration component storable in memory andexecutable by the at least one processing unit, the integrationcomponent comprising: an object definer configured to define anintermediate data table that is separate from a target storagedestination built from target metadata of the target storage destinationcontaining a set of data objects; a table loader configured to load amapped data set containing a set of source data objects from a sourceinto the intermediate data table based on a configuration object; asorter configured to: sort the set of data objects built from targetmetadata; generating a target data snapshot that represents an entirerecord set from which the set of data objects is a part; and sort andvalidate source data in the intermediate data table using the targetmetadata; a comparer configured to compare the set of data objects tothe sorted and validated source data using delta processing to identifydifferences using the target metadata; a generator configured togenerate a set of database programming language statements to map thedifferences in the source data based on the target metadata using thetarget metadata; and a loader configured to load the mapped source datato a target storage destination using the set of programming languagestatements.
 7. The computer system according to claim 6, the loaderfurther configured to batch load the mapped source data to the targetstorage destination.
 8. The computer system according to claim 6, thecomparer further configured to categorize the set of data objects basedon the comparison of the set of data objects to the sorted and validatedsource data.
 9. The computer system according to claim 8, the comparerfurther configured to categorize the data objects into at least one ofthe following categories: failed validation, new records, updatedrecords, deleted records, and re-inserted records.
 10. The computersystem according to claim 6, the comparer further configured to performthe delta processing to identify the differences between the set of dataobjects and the sorted and validated source data.
 11. Acomputer-readable storage hardware device storing computer instructions,which when executed, enables a computer system to provide dataintegration using automated data processing based on target metadata,the computer instructions comprising: defining an intermediate datatable that is separate from a target storage destination built fromtarget metadata of the target storage destination containing a set ofdata objects; loading a mapped data set containing a set of source dataobjects from a source into the intermediate data table based on aconfiguration object; sorting the set of data objects built from targetmetadata and generating a target data snapshot that represents an entirerecord set from which the set of data objects is a part; sorting andvalidating source data in the intermediate data table using the targetmetadata; comparing the set of data objects to the sorted and validatedsource data using delta processing to identify differences using thetarget metadata; generating a set of programming language statements tomap the differences in the source data based on the target metadatausing the target metadata; and loading the mapped source data to atarget storage destination using the set of programming languagestatements.
 12. The computer-readable storage device according to claim11, the computer instructions for loading further comprisinginstructions for batch loading the mapped source data to the targetstorage destination.
 13. The computer-readable storage device accordingto claim 11, further comprising computer instructions for categorizingthe set of data objects based on the comparison of the set of dataobjects to the sorted and validated source data.
 14. Thecomputer-readable storage device according to claim 13, furthercomprising computer instructions for categorizing the data objects intoat least one of the following categories: failed validation, newrecords, updated records, deleted records, and re-inserted records. 15.The computer-readable storage device according to claim 11, furthercomprising computer instructions for performing the delta processing toidentify the differences between the set of data objects and the sortedand validated source data.